We build our approach on an LSTM-based encoder-decoder architecture. This network encodes the states of the predicted vehicle, as well as the track boundaries with LSTM layers. Unlike common approaches for road traffic [1]}, we avoid using an image-based map representation due to the simple track geometry, and instead make use of a much more efficient way by processing the left and right boundary similar to the ego state history in a vector representation. The different input streams are fused by concatenation in the latent space. In contrast to previous work [2]}, we do not use LSTM decoders that generate corresponding time-dependent points as trajectories. The use of LSTM decoders has the definite disadvantage that the predicted trajectories are often physically infeasible and leave the track, for example. Hence, we extend this purely data-based approach to include physical knowledge: We identify basic trajectories that completely cover the output space of predictions by linear combination. Consequently, the neural network directly learns not the time-dependent positions, but weighting factors for the base trajectories; We call this mixture of data-based and physical approaches MixNet, which is discussed in further detail in [3]}. Furthermore, to make use of observations from past laps in similar situations we investigated an additional online-learning approach [4]}, which adopts the weights in a neural network according to an observation loss. However, due to a lack of robustness (and the inability to recognize specific objects), eventually, we did not incorporate this online learning in the final software stack.
